Digital surveillance and algorithmic control reshape India’s future workplace relations

India’s gig economy is on course to swell to 23.5 million workers by 2029–30, yet the algorithms that govern these workers’ daily lives are systematically stripping away their autonomy and bargaining power, according to a new study that reframes platform labour as a fundamental employment relations crisis rather than a mere social policy problem. The research, published as an open-access review in Discover Global Society, argues that algorithmic management has become a form of “digital Taylorism” — a data-driven revival of the scientific management principles first articulated more than a century ago — and that addressing its consequences demands nothing less than a new social contract between platforms, workers, and the state.

The study, conducted by Sazzad Parwez of Woxsen School of Business at Woxsen University in Hyderabad, is based on a scoping review of 145 studies spanning the period from 2010 to 2025, the era that encompasses the rise of major platform companies and the scholarly and policy debates that followed. Published on 28 August 2026, the paper integrates precarious work theory, power dynamics, and core employment relations constructs — worker voice, collective agency, and dispute resolution — to build a conceptual framework for understanding the structural conditions of platform-mediated labour in India. Its central claim is provocative: the gig economy should not be analysed primarily through the lenses of labour economics or welfare policy, but through the analytical tools of employment relations, including collective bargaining, dispute resolution, and representation.

The theoretical heart of the paper lies in its account of algorithmic control. Drawing on the influential framework developed by Kellogg and colleagues, the study identifies three mechanisms through which algorithms now perform the functions of an employer. The first is algorithmic direction: the automated allocation of tasks through matching systems such as Uber’s surge pricing, Swiggy’s delivery routing, and Zomato’s restaurant assignment, which present workers with non-negotiable task parameters and remove any worker agency over the core terms of the working relationship. The second is algorithmic evaluation, in which automated rating systems create what researchers have called “manufactured consent” — workers internalise the demand to satisfy customer ratings as self-discipline without recognising it as employer performance management, and cannot challenge rating outcomes or access the algorithmic logic that produces them. The third is algorithmic discipline through deactivation: the platform equivalent of dismissal, typically communicated without explanation, without a right of appeal, and without statutory notice — conditions that would constitute unfair dismissal in any regulated employment context.

This tripartite control apparatus, the paper argues, achieves what Frederick Taylor’s scientific management once did — the decomposition of work into measurable, controllable tasks subject to constant surveillance — but without the transparency or contestability of human management. Workers are subjected to intensive algorithmic oversight while simultaneously classified as independent contractors, placing them outside the formal protections of employment law. The result is a structural paradox: platforms exercise extensive disciplinary authority while formally disclaiming the employer role, creating what legal scholars have termed a “regulatory gap” that existing employment law was never designed to address. The study extends the concept of the “fissured workplace” — the disaggregation of employment functions across contracting chains — to this algorithmic fragmentation, in which the employer function is distributed across algorithmic systems, legal ownership, and the workers themselves.

The consequences for workers are severe and measurable. The review confirms that 62 per cent of Indian gig workers experience moderate to severe stress, compared with 40 per cent of salaried workers, according to data from the Indian Institute of Public Health. Income volatility — the cumulative instability of monthly earnings — afflicts roughly 65 per cent of respondents in the datasets examined, driven by surge pricing fluctuations, order cancellations, and route inefficiencies over which workers exert no control. Platform companies have implemented unilateral pay cuts, such as Uber’s reductions in driver rates in India between 2014 and 2023, without consultation, negotiation, or recourse. The study distinguishes between unpredictable earnings at the task level and broader income volatility, noting that both reflect the absence of the wage negotiation mechanisms — minimum pay rates, overtime provisions, negotiated conditions — that collective bargaining institutions provide in unionised industries.

Perhaps the most striking analytical contribution of the paper is its treatment of India’s distinctive institutional context. Approximately 90 per cent of Indian workers operate outside formal employment protections, which means that for many gig workers, platform work represents not a degradation from formal employment but a formalisation of existing informal labour arrangements. This generates a theoretically important paradox: when algorithmic management is superimposed on a labour market already characterised by unregulated and often exploitative work, it does not simply displace a prior social contract — it constitutes a new digital structure grafted onto historically unprotected labour. The deactivation that would constitute unfair dismissal in Europe may, paradoxically, represent a form of accountability absent in the wholly unregulated informal sector. This insight challenges the direct application of Western-centric frameworks and demands contextually sensitive theory.

The paper also foregrounds intersectional vulnerability, showing how algorithmic systems reproduce and amplify existing social inequalities without any explicit discriminatory intent. Algorithmic systems typically process observable data proxies — geographic location, timing patterns, device type, order acceptance rate, customer ratings — rather than social identity directly. But these proxies are not socially neutral: location variables correlate with caste concentration in particular residential zones; timing patterns reflect gendered care responsibilities; device-type variables encode class and caste-linked digital access inequalities. Women constitute only 20 per cent of India’s gig workforce, reflecting safety concerns, care responsibilities, and the concentration of platform work in male-dominated sectors, and those who do participate face lower earnings and more adverse algorithmic treatment. Compounding this, Oxfam India reports that 70 per cent of Indian gig workers signing digital contracts did not fully understand their terms due to language barriers — a structural employment relations failure rather than a mere practical obstacle.

Yet the review also documents emerging forms of collective resistance. Ola and Uber driver strikes in Delhi in 2017, 2019, and 2022, and the formation of the Indian Federation of App-Based Transport Workers, represent embryonic forms of collective voice consistent with what theorists call “networked unionism” — organisational forms that leverage digital communication to overcome the spatial fragmentation of gig work. Platforms, however, have developed countermeasures that fundamentally alter the bargaining calculus of traditional industrial action: by drawing on waiting lists and activating dormant worker accounts during strikes, algorithmic systems can substitute labour at scale, undermining the scarcity power on which conventional collective action depends. This, the study notes, is a novel employment relations challenge that existing collective action theory does not adequately address.

Against this backdrop, the paper evaluates India’s rapidly evolving regulatory landscape. Karnataka’s 2025 gig worker legislation introduced a welfare fund, transparency requirements, and a grievance redressal mechanism — the last being a genuine employment relations innovation, creating an institutional dispute resolution process for a category of workers previously without any such access. More significantly, the four new Labour Codes enacted with effect from 1 April 2026 consolidate India’s labour law architecture, with the Code on Social Security extending provident fund, gratuity, employee state insurance, and maternity benefit provisions to gig and platform workers. These developments mark a significant institutional shift, directly addressing what the paper calls the redistribution dimension of a new social contract. But the framework’s logic is uncompromising: the Labour Codes do not establish collective bargaining rights for platform workers or require algorithmic transparency. Redistribution without recognition, and recognition without representation, the paper argues, are analytically incomplete and practically insufficient.

The study’s proposed new social contract rests on three dimensions. Recognition requires legal innovation — a third category of “dependent contractor” or “worker” that triggers specified obligations such as minimum income, anti-discrimination protection, and statutory grievance rights, without requiring the full apparatus of employment law, alongside a judicial interpretation of “employer” that captures the functional employer role of algorithmic management systems. Representation demands mandatory algorithmic transparency, requiring platforms to disclose the criteria and weightings that determine task allocation, performance ratings, and deactivation; worker consultation rights in algorithm design and modification; and statutory grievance procedures with access to independent adjudication. Redistribution requires platform companies to contribute to portable benefit systems that follow workers across platform engagements, with mandatory contribution rates, portability, and worker governance of welfare funds. The paper also introduces the concept of “algorithmic breach” — unilateral modification of the terms of platform work through opaque algorithm changes — as a new category of psychological contract violation with no contractual remedy.

The implications extend beyond India. The EU’s presumption of employment rule, adopted in 2024, shifts the burden of proof onto platforms to demonstrate that workers are genuinely self-employed, while California’s AB5 experiment and its partial reversal by Proposition 22 illustrate the fierce political economy of regulatory reform. The Indian case, with its convergence of informality, rapid digitalisation, and weak institutional frameworks, offers a uniquely generative test case: where gig work in the West emerged as a supplement to mature welfare states, India’s platform economy is growing within an environment of extensive informal labour markets and nascent regulation. The paper concludes that whether the gig economy’s growth occurs under conditions of deepening precarity or through a genuinely new social contract depends not only on policy choices but on the quality of the theoretical frameworks available to analyse, evaluate, and challenge the employment relations of platform capitalism. As generative AI and automation reshape labour market demands at unprecedented pace, the stakes of getting that framework right — for India’s 23.5 million future gig workers and for platform labour movements worldwide — could scarcely be higher.